In a conventional method of segmenting multi-view images into foreground and background, multi-view images are segmented into the foreground and the background by modeling the background based on brightness values of pixels.
However, since a complicated background is not easy to model based on brightness values, it is modeled using a Mixture of Gaussian (hereinafter referred to as ‘MOG’) method. The MOG method is disadvantageous in that rapid variation in the background is difficult to be modeled using a small number of Gaussians. If the number of Gaussians is increased and the learning ratio is set for rapid variation in order to solve this problem, a problem arises in that a slowly varying background is detected as the foreground.
Meanwhile, as an actually applied technology, there is a codebook method which focuses on the speed. In this codebook method, a background model is generated depending on whether predetermined conditions are fulfilled. The codebook method is disadvantageous in that it does not generate an accurate model. Although the codebook method is advantageous in terms of the speed, it is also disadvantageous in terms of time because redundant background models (i.e., codewords in a codebook) can be generated and, therefore, many codewords need to be searched for.
Furthermore, the amount of memory used is not taken into consideration in the codebook method. That is, when pixel-based background models are generated, the amount of memory used is increased in proportion to the size of an image. The above-described algorithms are chiefly applied only to a single-view image. If an algorithm intended for a single-view image is applied to multi-view images, the amount of memory used is increased in multiples of the number of views. If the quality of such multi-view images is equal to or higher than High-Definition (HD), the amount of memory used is enormously increased, so that setting a limit on the amount of memory used is required. From the viewpoint of system configuration for actual applications, the amount of memory is inevitably limited because of hardware and the operating system.
For example, in ordinary 32-bit Windows-series operating programs, the amount of memory used is limited to a maximum of 2 GB. If an application program using foreground/background segmentation software is used, the use of memory allocated to the foreground/background segmentation software is limited. If the application program uses 1 GB, the foreground/background segmentation software can use a maximum of 1 GB. Assuming that twenty HD cameras are used for multiple views, it can be expected that about 50 MB of memory is available for one view.
As described above, if the conventional method of segmenting a single-view image into the foreground and the background is applied to multi-view images, the amount of memory used is limited. If an application program using the foreground/background segmentation software uses a maximum of 1 GB or the number of HD cameras exceeds 20, the amount of memory used is inevitably further reduced.